1. Introduction. V.S.S. Kiran 1, Y.K. Srivastava 2 and M. Jagannadha Rao 3

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1 Cloud Publications International Journal of Advanced Remote Sensing and GIS 2014, Volume 3, Issue 1, pp , Article ID Tech-273 ISSN Case Study Open Access Utilization of Resourcesat LISS IV Data for Infrastructure Updation and Land Use/Land Cover Mapping - A Case Study from Simlipal Block, Bankura District, W. Bengal V.S.S. Kiran 1, Y.K. Srivastava 2 and M. Jagannadha Rao 3 1 IIC Academy, IIC Technologies Ltd., Visakhapatnam, Andhra Pradesh, India 2 Scientist-SE, ISRO, RRSC- East, Kolkata, West Bengal, India 3 Delta Studies Institute, Andhra University, Visakhapatnam, Andhra Pradesh, India Correspondence should be addressed to V.S.S. Kiran, Publication Date: 26 May 2014 Article Link: Copyright 2014 V.S.S. Kiran, Y.K. Srivastava and M. Jagannadha Rao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Rapid population growth and anthropogenic activities on earth is effecting the natural environment profoundly. Hence, an attempt has been made in this paper; a case study has been taken up for Simlipal block of Bankura District of W. Bengal. This is to understand changes in Land use/land cover and infrastructure development particularly in plain and community development area. For this purpose the infrastructure, Land use/land cover, drainage, slope, aspect and contour maps have been prepared using SRTM (54/08) data of the study area. Besides this an attempt has been made to prepare LU/LC maps from multispectral remote sensing digital data sets of IRS-1C LISS-III & IRS-P6 LISS-IV, applying DIP techniques and Alarm masking technique for MAXLIK & MINPAR supervised classification as well as to prepare Infrastructure map applying to raster based vector classification and spatial data extraction method. NDVI method was used for the classification of water and forest classes. It is established that the Infrastructure output map and Land use/land cover output maps can be used for systematic urban development of the study area. Keywords Alarm Masking; LU/LC Classification; Arc GIS; ERDAS 1. Introduction Land is one of the most important natural resources and hence all developmental activities are based on it. Landuse refers to the type of utilization to which man has used the land for his daily activities like socio economic activity, urban and agricultural activity and this evaluation of land with respect to various natural characteristics. But the Landcover refers to the type of land which is covered by the physical material at the surface of the earth. Landcover includes grass, asphalt, trees, forest, built up area, bare ground, water, lake etc. Landuse/Landcover and infrastructure are essential for planners, decision makers and those concerned with land resource management also this valuable information is very much helpful for monitoring and sustainable management of the urban environment (Jensen,

2 J.R, 1999). Rapid population growth and anthropogenic activities have significant impact on our ecosystems and its present conditions. Accurate and updated information on the status and trends of ecosystems is required to develop strategies for sustainable development and to improve the livelihood. The ability to monitor land-cover/land-use & infrastructure is highly desirable by local communities and by policy decision makers. With increased availability and improved quality of multispatial remote sensing data as well as ground details and new analytical techniques, it is now possible to monitor changes in Landuse/Landcover and infrastructural developmental data. 2. Study Area Simlapal is a community development block is in Khatra subdivision of Bankura district in West Bengal state, and it is bounded by the Khatra block in west, Taldangra block in north, Sarenga block is south and also covered west Midnapur district in east. The block consists of rural areas with seven gram punchayats (Bikrampur, Dubrajpur, Lakshmisagar, Machatora, Mondalgram, Parsola and Simlapal) covered 203 villages, two police stations and three headquarters. Area of this block is sq. kms (119 sq. mile or hectares). Location Map of study area is presented in Figure Geographic Location This block is geographically extended from North to North latitude and to East longitudes. It has an average elevation 57 mts (187 feet). This block is covered by 73J/13 & 73N/1 Survey of India reference maps on 1:50,000 scale. Bankura district is lies on the western part of West Bengal having 7.75% of state s geographical area and 3.98% of states demographic profile. Figure 1: Location Map of the Study Area 2.2. Demography As per 2001 census Simlapal community development block has a total population of 1,27,429 of which 65,328 males and 62,101 females. The population density is per sq. kms area and its growth rate is 14.48% from the period of Simlapal has also the population of total schedule International Journal of Advanced Remote Sensing and GIS 593

3 caste and schedule tribe. The population of schedule caste 33,461 and schedule tribe is 18,821. As the sex ratio of 952 females per 1000 males in 2001 census and 953 females per 1000 males in 1991 census. Literacy level of the block is 71% in 2001 census. The male literacy is 58% of total male population of Simlapal and female literacy is 76% of total female population of Simlapal Climate Climate condition of the Simlapal block is tropical as situated in the southeastern part of Bankura district. Seasonal climatic conditions are; winter from November to February, summer from March to May and Rainy season from June to October. The average rainfall is about 1750 mm out of which 1025 mm during June to September. The wind speed of this block varies from time to time and it is atmospheric depended which plays very vital role to contribute in environmental activities. The maximum wind speed is 3.42 km/hr in the month of July and minimum 0.03 km/hr in the month of January. The maximum average wind speed is 1.38 km/hr in the month of July. The maximum temperature of area is 44.6 c and minimum temperature of the area 6.7 c. 3. Methodology Remote Sensing and GIS tools have been used for the processing of digital images and preparing of thematic maps. GIS was used as added tool for preparation of many vector layers. Using all thematic maps and GIS information. IRS LISS-IV Data geometrically corrected with reference to already geocorrected IRS LISS-III Data keeping RMS Error within the range of sub-pixel and output image was resample using nearest neighborhood resampling method (Congalton, K., and Green, A., 1999). The Lambert Conformal Conic projection was used with Everest coordinate system. An AOI (Area of interest) layer of the study area was prepared and applied to IRS LISS-IV data for extraction of the study area. Few enhancement techniques were applied to visually enhance the quality of the image. It was found that linear enhancement algorithms were best suited to identify various features of the study area as well as tonal boundaries of look-alike features. The methodology can be divided into two parts one is rasterization and other one is vectorization. The vectorization process created vector coverages like; administrative boundaries (i.e. block and village boundaries), drainage layers, infrastructure layer (i.e. metal & un-metal roads, water bodies, settlement, canal, sluice, river) and also forest boundaries etc. The rasterization involves creation of sub-setting of image, mosaicking, image enhancement, NDVI techniques, image classification, recoding and reclassification etc., (Lunetta, R., 2006). The calculation parameters were derived from the generated raster and vector layers. For infrastructure layer extraction purpose proper enhancement techniques were applied to enhance the details of drainage with shuffling of different band combination like 4, 1, 2 and 2, 1, 3 and 4, 1, 4. This improves visualization of drainage. LISS IV data was classified using supervised classification techniques with maximum likelihood algorithm for the preparation of land use/ land cover map. The classification of the imageries was performed by using supervised classification. In this particular type of classification signature extraction are first, based solely on the DN information in the data, and are then matched by the analyst to overall image using Alarm masking technique. Supervised classifiers is utilize training sets basis for classification. Rather it involve algorithms called Maximum likelihood or Minimum Parallelepiped algorithms, that examine the known pixels in an image and aggregate them into a related classes based on the user selection in the image values (Kumar, P., 2010). Thus supervised classification, it starts with a pre-determined set of classes, and it is done completely with human intervention. The entire methodology which has been adopted in this study is explained in the flow chart (Figure 2). Source data details are presented in table (Table 1). The study area is covered by 73J/13 & 73N/1 Survey of India Toposheets on 1:50,000 scales and IRS LISS III & IV satellite imagery with 23.5 and 5 meter resolutions, which was acquired on 17th February 2003 and 21st January 2007 with path and row of 107/56 & 102/56 were used as source data. International Journal of Advanced Remote Sensing and GIS 594

4 Figure 2: Methodology of Study Table 1: Land Use-Land Cover Classification Scheme Type of Data Details of Data Source of Data SOI Reference Maps 73 J/13, 73 N/1, (scale 1:50,000) SOI, RRSC-East Thematic maps: Soil Map (Scale 1:5,00,000) NBSS/LUP,RRSC-E DEM DATA(SRTM) 90mtr Resolution, Path/row - 54/08 SRTM Website Remote Sensing digital data sets of IRS-1C-LISS-III & IRS-P6- RESOURCESAT LISS-IV LISS-III LISS-IV Scene Date Scene Date 107/55 04/03/ /79 23/12/ /56 17/02/ /80 23/12/ /56 21/01/2007 Regional Remote Sensing Service Center (RRSC-E), 3. Results, Discussion and Conclusion A NDVI (Normalized Difference Vegetation Index) indices was performed to derive the class in the forest area and water-bodies. As all the LISS IV scenes were acquired in the different time interval hence, each was separately used for NDVI and then desired classes were sliced while clubbing other classes. Final NDVI map was overlaid on the classified image to represent the classes which were not considered during the supervised classification. A supervised classification technique was adopted with maximum likelihood algorithm. Due care was taken in generating the signature sets for the International Journal of Advanced Remote Sensing and GIS 595

5 desired classes and where validated with the error of omission and error of commission. Wherever, overlapping of signatures was found, new sets of signatures were generated to improve the classification of LISS-IV image. Basic visual and digital interpretation parameters were followed like; tone, texture, shape, size, pattern, location and association for the recognition of objects and their tonal boundaries. Further refinement was carried out in the classified image with filtering and recoding of few classes. The final classified output image was assigned 13 classes (Table 2). Validation was performed with respect to SOI reference maps and other collateral data. Overall good accuracy of 90-95% was achieved (Figure 3). Also using resource sat data, we have extracted the infrastructure layer like i.e., metal & un-metal roads, water bodies, settlement, canal, sluice, river etc., and these all layer overlaid into the village boundary map and generated infrastructure layer (Figure 4). The current Land use and land cover data and infrastructure data can be used by State government and local agencies for effective water-resources inventory, flood control, water-supply planning, and waste-water treatment and irrigation planning and other agricultural activities. Table 2: Land Use- Land Cover Classification Scheme Code Land Use/Land Cover Categories Code Land Use/Land Cover Categories 1 Agriculture 8 Forests Blank 2 Plantation 9 Degraded Forest 3 Fallow 10 Dense Forest 4 Scrub land 11 River 5 Wasteland 12 Sand Deposition 6 Water bodies 13 Settlement s 7 Open Forest Figure 3: LU/LC Map of Study International Journal of Advanced Remote Sensing and GIS 596

6 Figure 4: Infrastructure Map Acknowledgement The author is thankful to the Region Remote Sensing Centre - East, Kolkata, India for providing the satellite and collateral data and thankful to the IIC Technologies Ltd for providing financial support. Dr. V.M. Chowdhury, Scientist, RRSC-E, Dr. A. Jayaram Ex. GM, RRSC - East and Shri. Shekhar Murthy, President, IIC Academy, Shri. Rajesh Alla, MD, IIC Technologies ltd and overall IIC team for extending all the necessary facilities to complete this work. References Aggarwal, S., 2003: Principles of Remote Sensing. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. Proceedings of a Training Workshop, 7-11 July, Dehra Dun, India, Congalton, K., and Green, A. 1999: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, New York. Lewis Publisher. Kumar, P. Monitoring of Deforestation and Forest Degradation Using Remote Sensing and GIS: A Case Study of Ranchi in Jharkhand (India). Report and Opinion, (4). Lunetta, R. Land-Cover Change Detection Using Multi-Temporal Modis NDVI data. Remote Sensing of Environment ; Lillesand, T.M., and Kiefer, R.W., 2001: Remote Sensing and Image Interpretation. John Wiley and Sons, Hoboken, NJ. Jensen, J.R., and D.C., Cowen. Remote Sensing of Urban/Suburban Infrastructure and Socio- Economic Attributes. Photogrammetric Engineering & Remote Sensing ; International Journal of Advanced Remote Sensing and GIS 597

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